The present invention relates in general to tracking of targets by means of measurements from various sensors and in particular to automatic sensor alignment.
Target tracking is the process by which the location and motion of objects like aircraft, ships, missiles, etc., are established and presented to users, for example, situated at a command and control centre. The detection of the targets is accomplished by one or more sensors. The most widely used sensor for targets in the air is the radar. However, radars are not the universal solution since they are impaired by serious inherent drawbacks. At the state of the art, their accuracy is limited. Moreover, they can""t be used under water. Additionally, by radiating energy, radars reveal their locations and form easy targets for enemy forces. Depending on the particular application, one therefore uses other types of sensors instead of or in complement of radars. Examples of such sensors, which, however, should not be considered as a complete set of possibilities, are Global Navigation Satellite System (GNSS) and its predecessors GPS and GLONASS, Electronic Support Measures (ESM) equipment, Electro-Optical (EO) sensors and their subclass Infrared Search and Track (IRST), Jam strobe detectors (ECM), air pressure based altitude measuring equipment downlinked via xe2x80x98Secondary Radarsxe2x80x99 (Mode C), as well as active and passive sonars. Out of these sensors, only certain radars (3D) and the satellite based navigation data provide full three-dimensional determination of the target location. ESM, EO, IRST, ECM, and passive sonars only give the direction to the target. Such measurements are referred to as xe2x80x9cstrobesxe2x80x9d.
The sensors produce measurements at regular or irregular time intervals. A measurement is a piece of information including e.g. range and azimuth of the target. A tracker is a device, which will create a track file for each detected target. A track is a set of usually filtered data which are associated with a certain target. The tracker updates the track data with incoming measurements in such a way that measurement noise is reduced by filtering, and speed and heading is computed. The track is given a label and is presented to the operators. The general art of tracking is well known and can be found at many different places, e.g. in S.S. Blackman xe2x80x9cMultiple-Target Tracking with Radar Applicationsxe2x80x9d, Artech House, Inc, MA, USA 1986, in particular pages 1-17 and 357-393.
There are many reasons to use multiple sensors for target tracking. Evidently, the combination of passive sensors at different locations makes it possible to obtain the distance to the target. By using several radars, gaps in coverage of individual radars may be eliminated. Moreover, satellite data will improve accuracy, if available. ECM gives information even if a jammer on the target makes the radar data useless, and so forth. In general, several sensors contribute to more frequent updates, better accuracy, higher signal to noise ratio, and more reliable tracking.
There is, however, a serious problem in combining data from several sensors: alignment errors, also known as systematic errors, bias errors, or registration errors. The alignment errors may give the impression that the number of targets is larger than the actual number because the measurements are displaced by some, often very significant, amount. Even if there is only one track per target, accuracy will clearly be impaired. Therefore, estimation and compensation of the bias errors are necessary for successful tracking.
To illustrate these problems, a simple example will be discussed. FIG. 1 shows an example of a tracking situation. The tracking system have measurements available from two 3D sensors, D13 and D23, respectively, providing range, azimuth as well as height information, and one passive sensor D1, only providing azimuth angle measurements. One true target X is present in the area covered by the system. The sensors exhibit some systematic errors. The local coordinate system of sensor D13 is misaligned with respect to the common coordinate system with a certain angle xcex1. The sensor D23 has an error in the range measement, which gives rise to systematic errors in the range values. In the situation in FIG. 1, sensor D13 detects a target in the direction of a real azimuth angle AR13, but due to the misalignment, the apparent azimuth angle AA13 is shifted an angle xcex1, so the apparent location for the detected target is M13. The range measuring error of sensor D23 causes the apparent target position as measured by sensor D23 to be M23, offset a distance r along the azimuth angle from the true position. Sensor D1 provides a true azimuth angle measurement. In this situation, it is probable that a target tracking system will interpret the situation as if two targets are present, at M13 and M23. The M13 position is reasonably consistent with measurements from both D13 and D1, while the M23 position is far away. From this example it is obvious that there is a need for some alignment procedure.
It turns out that the bias estimation or automatic alignment is quite a difficult task, and it is further complicated by drifts in electronic and/or mechanical systems, which call for recurrent recalibration. The methods existing so far have various severe limitations. Some approaches according to prior art are described below.
A first approach concerns estimating and compensating for the deviations in x, y and z for each single track separately. The patent DE 3,132,009 gives an example of this approach. The drawback is that the estimation process is initiated for each track, and it may take considerable time before the estimates are good enough for accurate tracking.
Most other methods aim at modelling each sensor with a set of bias parameters. In this document, the term xe2x80x9cbias parametersxe2x80x9d is used for parameters, each of which represents a characteristic of the sensor like a location error, a north alignment error, or a range offset etc. Another term used in the literature, basically denoting the same measures, is xe2x80x9cregistration errorsxe2x80x9d. Most methods according to the state of the art handle a very limited set of sensor types and a very limited set of parameters.
A second approach of automatic alignment uses the above described principle which is applied by using reference targets with exactly known positions. By comparing a set of measured positions with a set of true positions one can adapt the bias parameters so that the deviations are minimised. There are, however, severe disadvantages. The positions of the reference targets have to be measured very accurately. The position of the reference target should optimally also be situated somewhere in the volume, where normal targets appear, which often may be over sea or even over international or other nations territories. Fixed reference targets with well defined positions are also normally restricted to ground level or close to ground level, which makes calibrations of altitude and inclination measurements difficult to perform. Moreover, reference targets are also easily destroyed or manipulated with during hostile situations.
In the absence of reference targets, a third approach involves designation of one of the sensors as a reference sensor, and alignment of the others with respect to the reference. One example of such a system is found in the proceedings of the SPIExe2x80x94The International Society for Optical Engineering, Vol. 2235, 1994, in an article in the name of R. Helmick et al, with the title xe2x80x9cOne-step fixed-lag IMM smoothing for alignment of asynchronous sensorsxe2x80x9d, pages 507-518. Here a method for compensating a number of 3D sensors is disclosed. Each sensor has its own tracker and the filtered data is brought to a common time. One sensor is selected as a master and the tracks of the other sensors are compared with the master to calculate correction factors. This method requires 3D sensors since each sensor has its own complete track. Furthermore, the selection of a master also presents problems if it happens to have relatively large systematic errors itself or if it at any occasion is not working properly, problems which are common for all systems working according to this approach. Finally, this particular method does only discuss range, north alignment, inclination and altitude errors of the sensors.
In the above described example, shown in FIG. 1, it is obvious that neither sensor D13 nor sensor D23 are suitable as reference sensors. Furthermore, sensor D1 can not be used as reference, since it is unable to provide a complete target track on its own.
There are also methods in prior art, which do not to have one sensor as reference or do not use a reference target. These methods may be collected in a fourth approach. Bias parameters will then be estimated for all the participating sensors. In the proceedings of the International Conference on Radar, Paris May 3-6 1994, an article with the title xe2x80x9cLeast squares fusion of multiple radar dataxe2x80x9d, page 364-369, in the name of H. Leung et al discloses such a method for bias compensation of data from two 2D radars. Values of the range and north misalignment errors, which gives the lowest feast squares discrepancy for the radar measurements, are calculated. Poorly conditioned systems of equations are handled by singular value factorisation. Only one single pair of 2D radars is discussed, and only range and north misalignment errors are compensated for.
In xe2x80x9cMaximum Likelihood Registration of Dissimilar Sensorsxe2x80x9d by Daniel W. McMichael and Nickens N. Okello, Proceedings of First Australian Data Fusion Symposium (ADFS-96), Adelaide November 21-22, 1996, page 31-34, another method for automatic alignment of this approach is disclosed. The method comprises a procedure for maximising a probability function using a maximum likelihood approach. The procedure is performed in an iterative manner to obtain a requested accuracy. The disadvantages with this particular method is that it does not give any clue about how to use measurements performed at different times. The method is furthermore an off-line method, since it does not handle any time propagation features.
In general, systems according to the fourth approach, that do not have a reference sensor tend to give ill-conditioned estimation problems as will be explained in the sequel, and one has to be very restrictive in the selection of bias parameters.
In prior art, there are also techniques which are somewhat similar, but not directly related to the present scope of concerns. In the U.S. Pat. No. 5,208,418, the relative alignment of sensors for different weapons are discussed. It discloses a method for compensation of solely misalignments between pairs of sensors and guns. The method does not concern multi-sensor tracking. The U.S. Pat. Nos. 5,072,389 and 5,245,909, disclose methods for compensation of dynamic, rapidly changing errors using accelerometers, which are not of interest within the scope of the present invention.
An object of the present invention is to provide a method for sensor alignment, which is not limited by the use of a fixed reference sensor or reference target, and which is applicable to all combinations of sensors, passive or active.
Another object of the present invention is to provide a method for sensor alignment, which is able to estimate a large set of bias parameters.
Yet another object of the present invention is to provide a method for sensor alignment, which provides updated estimates whenever requested, regularly or occasionally.
Still another object of the present invention is to provide a method for sensor alignment, which enables handling of each bias parameter with individual time characteristics.
The objects of the present invention is achieved by a process exhibiting the features set forth in the claims. The process of the invention repeatedly generates estimates for sensor bias errors by minimising a function, given on one hand by the magnitude of the discrepancy between measurements and a measuring model, where the measuring model is a function of the unknown target location and unknown bias parameters, and on the other by the bias parameters and their predetermined statistical distributions. In a preferred embodiment of the present invention, the minimising step is performed by linearising the function around an approximate target position (normally obtained from the tracker) and around nominal (typically zero) bias errors, and the function is subsequently minimised with respect to target positions as well as to the bias parameters. In addition, possible time dependence of the bias parameters are modelled by the incorporation of process noise.